Abstract
There is a need of a method or an application that can recognize sign language gestures so that the communication is possible even if someone does not understand sign language. With this work, we intend to take a basic step in bridging this communication gap using Sign Language Recognition. Video sequences contain both the temporal and the spatial features. To train the model on spatial features, we have used inception model which is a deep convolutional neural network (CNN) and we have used recurrent neural network (RNN) to train the model on temporal features. Our dataset consists of Argentinean Sign Language (LSA) gestures, belonging to 46 gesture categories. The proposed model was able to achieve a high accuracy of 95.2% over a large set of images.
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Masood, S., Srivastava, A., Thuwal, H.C., Ahmad, M. (2018). Real-Time Sign Language Gesture (Word) Recognition from Video Sequences Using CNN and RNN. In: Bhateja, V., Coello Coello, C., Satapathy, S., Pattnaik, P. (eds) Intelligent Engineering Informatics. Advances in Intelligent Systems and Computing, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-10-7566-7_63
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